I am using GPUImage to process incoming video and I would like to then consider a given square subregion of the image and determine the average pixel value of the pixels in that region. Can anyone advise me on how to accomplish this? Even information on how to acquire pixel data for a pixel at coordinate(x,y) in the image would be useful.
Apologies if this is a simple question, but I am new to computer vision and the way to do this was not clear to me from the available documentation. Thank you.
First, use a GPUImageCropFilter to extract the rectangular region of your original image. This uses normalized coordinates (0.0 - 1.0), so you'll have to translate from the pixel location and size to these normalized coordinates.
Next, feed the output from the crop filter into a GPUImageAverageColor operation. This will average the pixel color within that region and use the colorAverageProcessingFinishedBlock that you set as a callback. The block will return to you the average red, green, blue, and alpha channel values for the pixels in that region.
For an example of both of these operations in action, see the FilterShowcase example that comes with the framework.
Related
I'm working on a project which locates the Machine Readable Zone on ID cards.
For this I need to do some pre processing to extract the ID card from a scanned image which typically are randomly disposed on a white page. I'm able to locate the majority of the cards by using a Histogram equalization with CLAHE before a contour detection. But in some cases the border around the MRZ is totally invisible (white on white) as shown on the attached image.
I'd like to detect rectangle of a predefined shape as I know the shape of the ID card will be always the same but so far I wasn't able to find a way do do something like this with OpenCV.
Basically what I need is to find two rectangle of a fixed ratio that best match the 2 cards on the scan.
I'm wondering if I need to try OpenCV matchers or if there is a simpler way to accomplish this kind of detection.
The solution to you problem is likely going to be matrix transformations. The concept is to pinpoint 4 coordinates on the card that can be easily detected using opencv, such as the the rectangle colored in blue & cyan.
Have coordinates of the card with the predefined shape stored in an array, where a corner of the card is at the 0, 0. Also store the coordinates of the blue * cyan rectangle in an array. With the two arrays you can find the perspective transform of the two arrays using the cv2.getPerspectiveTransform method.
Using the perspective transform found, you can detect the coordinates of the whole card every time you detect the coordinates of the blue & cyan rectangle.
I'm using an app for face redaction that doesn't allow access to the source code but only allows me to pass pixel values for red, green and blue channel upon which it creates a matrix with the same average RGB values for every ROI pixel value. For eg. if I give Red=32,Blue=123 and Green=233 it will assign these RGB values for every pixel of the ROI and then draws a colored patch on the face.
So I was wondering is there a general combination of RGB values of a pixel to distort it and make it look like it's blurred. I can also set the opacity value in the app.
Thanks.
I would like to use GPUImage's Histogram Equalization filter (link to .h) (link to .m) for a camera app. I'd like to use it in real time and present it as an option to be applied on the live camera feed. I understand this may be an expensive operation and cause some latency.
I'm confused about how this filter works. When selected in GPUImage's example project (Filter Showcase) the filter shows a very dark image that is biased toward red and blue which does not seem to be the way equalization should work.
Also what is the difference between the histogram types kGPUImageHistogramLuminance and kGPUImageHistogramRGB? Filter Showcase uses kGPUImageHistogramLuminance but the default in the init is kGPUImageHistogramRGB. If I switch Filter Showcase to kGPUImageHistogramRGB, I just get a black screen. My goal is an overall contrast optimization.
Does anyone have experience using this filter? Or are there current limitations with this filter that are documented somewhere?
Histogram equalization of RGB images is done using the Luminance as equalizing the RGB channels separately would render the colour information useless.
You basically convert RGB to a colour space that separates colour from intensity information. Then equalize the intensity image and finally reconvert it to RGB.
According to the documentation: http://oss.io/p/BradLarson/GPUImage
GPUImageHistogramFilter: This analyzes the incoming image and creates
an output histogram with the frequency at which each color value
occurs. The output of this filter is a 3-pixel-high, 256-pixel-wide
image with the center (vertical) pixels containing pixels that
correspond to the frequency at which various color values occurred.
Each color value occupies one of the 256 width positions, from 0 on
the left to 255 on the right. This histogram can be generated for
individual color channels (kGPUImageHistogramRed,
kGPUImageHistogramGreen, kGPUImageHistogramBlue), the luminance of the
image (kGPUImageHistogramLuminance), or for all three color channels
at once (kGPUImageHistogramRGB).
I'm not very familiar with the programming language used so I can't tell if the implementation is correct. But in the end, colours should not change too much. Pixels should just become brighter or darker.
I have a photo editing app that is built around Brad Larson's amazing
GPUImage framework.
I need a way to analyze an image's histogram so i can return it's range.
Meaning the real range of "activity".
I need this to improve a tool i have in the app that controls the RGB composite curve.
Is there a way to analyze or retrieve hard numbers from the histogram filter in GPUImage ? any other way to do it?
As I document in the Readme.md for the GPUImageHistogramFilter:
The output of this filter is a 3-pixel-high, 256-pixel-wide image with
the center (vertical) pixels containing pixels that correspond to the
frequency at which various color values occurred. Each color value
occupies one of the 256 width positions, from 0 on the left to 255 on
the right. This histogram can be generated for individual color
channels (kGPUImageHistogramRed, kGPUImageHistogramGreen,
kGPUImageHistogramBlue), the luminance of the image
(kGPUImageHistogramLuminance), or for all three color channels at once
(kGPUImageHistogramRGB).
To get the numerical values for the histogram, have the GPUImageHistogramFilter output to a GPUImageRawDataOutput and grab bytes from that. The result will be a 256x3 (width, height) array of 0-255 values indicating the intensity at each color component. You can ignore the first and last rows, as the values are only present in the center row.
From there, you can analyze the histogram obtained by this operation.
Please can some one explain how to identify area which are should in red and blue colors in following image ? I tried to use cvFindContours() method but it didn't give expected result for me.
Input image
Expected result
I Like to know whether there are any other methods to identify or calculate the area of this kind of contours. Please be kind enough to share simple code example with this.
The function floodFill can also return an area as its return value. So one thing you can do is to raster scan each pixel: each time you reach untouched pixel, colour it into some colour(black), and store the area of that region along with the pixel coordinates, continue until whole image would not be covered.
In the end you will have a set of areas with cordinates for one pixel in each region.
Will you need to recover specific region you can use floodFill again by colouring that region to a specific colour.